Package {movegroup}

Title:	Visualizing and Quantifying Space Use Data for Groups of Animals
Version:	2026.07.01
Description:	Offers an easy and automated way to scale up individual-level space use analysis to that of groups. Contains functions from the 'move' package to calculate either a dynamic Brownian bridge movement model or dynamic Bivariate Gaussian bridges from movement data for individual animals, as well as functions to visualize and quantify space use for individuals aggregated in groups. Originally written with passive acoustic telemetry in mind, this package also provides functionality to account for unbalanced acoustic receiver array designs, and satellite tag data.
Depends:	R (≥ 4.1.0)
License:	MIT + file LICENSE
Encoding:	UTF-8
Language:	en-GB
LazyData:	true
Imports:	ggplot2 (≥ 3.3.5), utils, beepr (≥ 1.3), dplyr (≥ 1.0.8), ggmap (≥ 3.0.0), knitr (≥ 1.45), lubridate (≥ 1.8.0), magick (≥ 2.8.2), methods, move (≥ 4.1.6), purrr (≥ 1.0.2), raster (≥ 3.5-15), rlang (≥ 1.0.2), sf (≥ 1.0-7), sp (≥ 1.4-6), stars (≥ 0.5-5), starsExtra (≥ 0.2.7), stringr (≥ 1.4.0), terra (≥ 1.7-39), tidyr (≥ 1.2.0), tidyselect (≥ 1.2.0), viridis (≥ 0.6.4)
RoxygenNote:	7.3.2
Suggests:	rmarkdown
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2026-07-01 21:33:26 UTC; vital
Author:	Simon Dedman [aut, cre], Maurits van Zinnicq Bergmann [aut], Vital Heim [aut]
Maintainer:	Simon Dedman <simondedman@gmail.com>
Repository:	CRAN
Date/Publication:	2026-07-02 03:20:14 UTC

movegroup: Visualizing and Quantifying Space Use Data for Groups of Animals

Description

Offers an easy and automated way to scale up individual-level space use analysis to that of groups. Contains functions from the 'move' package to calculate either a dynamic Brownian bridge movement model or dynamic Bivariate Gaussian bridges from movement data for individual animals, as well as functions to visualize and quantify space use for individuals aggregated in groups. Originally written with passive acoustic telemetry in mind, this package also provides functionality to account for unbalanced acoustic receiver array designs, and satellite tag data.

Author(s)

Maintainer: Simon Dedman simondedman@gmail.com (ORCID)

Authors:

• Maurits van Zinnicq Bergmann mauritsvzb@gmail.com (ORCID)

• Vital Heim vital.heim@gmail.com (ORCID)

Data: Tracks of lemon sharks off Bimini, Bahamas

Description

Tracks of 17 lemon sharks (Negaprion brevirostris) tagged off Bimini, Bahamas, 2012:2014, by Bimini Biological Field Station employees and volunteers, with accompanying tidal phase.

Usage

data(TracksCleaned)

Format

A data frame with 1308 rows and 5 variables:

Datetime

POSIXct datetime, format YYYY-MM-DD HH:MM:SS.

Shark

Individual shark ID code.

T.Ph

Tidal phase, H M L High Medium Low.

Lat

Decimal latitudes.

Lon

Decimal longitudes.

Author(s)

Simon Dedman, simondedman@gmail.com

Maurits van Zinnicq Bergmann, mauritsvzb@gmail.com

Source

https://www.biminisharklab.com

Combines region-specific group-level UD rasters into a single raster.

Description

Extends the spatial extent of each area-specific group-level raster to the spatial extent shared by all rasters. This will only be required if you have multiple individuals (e.g. different sharks) divided amongst a few discrete areas (e.g. around different islands) and the effort (e.g. receiver coverage) is different among islands. Not required for multiple individuals all within the same region or sampling regime.

Usage

alignraster(
  folderroots = c("/myfolder/H", "/myfolder/L", "/myfolder/M"),
  foldernames = c("H", "L", "M"),
  pattern = ".asc",
  scalefolder = "Scaled",
  scaledweightedname = "All_Rasters_Scaled_Weighted",
  savefolder = tempdir(),
  format = "ascii",
  datatype = "FLT4S",
  bylayer = TRUE,
  overwrite = TRUE,
  returnObj = FALSE
)

Arguments

Details

When used in a movegroup pipeline, the order would be: movegroup.R, scaleraster.R, alignraster.R if required, plotraster.R.

Value

Region-specific group-level UD rasters that share the same spatial extent.

Author(s)

Simon Dedman, simondedman@gmail.com

Maurits van Zinnicq Bergmann, mauritsvzb@gmail.com

Examples

# load data
data("TracksCleaned")
# loop movegroup and scaleraster through tide subsets
tide <- c("H", "M", "L")
for (i in tide) {
dir.create(file.path(tempdir(), i))
  movegroup(
    data = TracksCleaned[TracksCleaned$T.Ph == i, ],
    ID = "Shark",
    Datetime = "Datetime",
    Lat = "Lat",
    Lon = "Lon",
    savedir = file.path(tempdir(), i, "/"))
  
  scaleraster(path = file.path(tempdir(), i),
              crsloc = file.path(tempdir(), i))
}

alignraster(folderroots = file.path(tempdir(), tide),
            foldernames = tide,
            savefolder = file.path(tempdir(), "Aligned"))

Data: Tracks of two great hammerhead sharks with position confidence intervals

Description

Tracks of 2 great hammerhead sharks tagged in Jupiter, and The Keys, Florida, USA, in 2022 and 2023 respectively, by Saving The Blue (SavingTheBlue.org), filtered by argosfilter::sdafilter and with state space model applied using aniMotum package, using scripts by Vital Heim, see https://github.com/SimonDedman/SavingTheBlue/blob/main/R/06A_Filter_SPOT_data.R and https://github.com/SimonDedman/SavingTheBlue/blob/main/R/06B_CTCRW_SPOT_data_usin_animotum.R .

Usage

data(argosFiltered)

Format

A data frame with 1492 rows and 8 variables:

id

Character, shark ID.

date

POSIXct datetime, format YYYY-MM-DD HH:MM:SS.

lon

Decimal longitudes.

lon025

Decimal longitudes, lower 95% confidence interval bound.

lon975

Decimal longitudes, upper 95% confidence interval bound.

lat

Decimal latitudes.

lat025

Decimal latitudes, lower 95% confidence interval bound.

lat975

Decimal latitudes, upper 95% confidence interval bound.

Author(s)

Simon Dedman, simondedman@gmail.com

Vital Heim, vital.heim@gmail.com

Source

https://www.savingtheblue.org

moveLocError calculator for ARGOS or state space models resulting in 95percent latlon confidence intervals

Description

Builds a dataframe of original locations plus rowmeans of mean distance of location extremities lon975, lat; lon025, lat; lon, lat975; lon, lat025 from the centre point lon, lat.

Usage

moveLocErrorCalc(
  x,
  loncol = "lon",
  latcol = "lat",
  latloncrs = 4326,
  projectedcrs = 32617,
  lon025 = "lon025",
  lon975 = "lon975",
  lat025 = "lat025",
  lat975 = "lat975"
)

Arguments

Details

Use on your data object from movegroup::movegroup(data).

Value

Dataframe of original locations plus rowmeans of mean distance of location extremities, for use in movegroup::movegroup(moveLocError).

Author(s)

Simon Dedman, simondedman@gmail.com

Examples

data(argosFiltered)
myMoveLocError <- moveLocErrorCalc(argosFiltered)

Automates dynamic Brownian bridge movement model and dynamic bivariate Gaussian bridge construction across individuals

Description

Automates dynamic Brownian bridge movement model (dBBMM) and dynamic bivariate Gaussian bridge (dBGB) calculation for utilization distribution (UD) estimation for multiple individuals simultaneously, using functions in the 'move' package. The authors are indebted to the move package authors Bart Kranstauber, Marco Smolla, and Anne K Scharf, and to Sarah Becker for seed code which inspired the development of the movegroup::movegroup function.

Usage

movegroup(
  data = NULL,
  model = "dbbmm",
  ID = NULL,
  Datetime = NULL,
  Lat = NULL,
  Lon = NULL,
  dat.TZ = "US/Eastern",
  proj = sp::CRS("+proj=longlat +datum=WGS84"),
  sensor = "acoustic-telemetry",
  integrationStep = NULL,
  moveLocError = 1,
  timeDiffLong = 2,
  timeDiffUnits = "hours",
  center = TRUE,
  centre = NULL,
  buffpct = 0.3,
  rasterExtent = NULL,
  rasterCRS = sp::CRS("+proj=utm +zone=17 +datum=WGS84"),
  rasterResolution = 50,
  movemargin = NULL,
  dbbext = 3,
  dbbwindowsize = NULL,
  writeRasterFormat = "ascii",
  writeRasterExtension = ".asc",
  writeRasterDatatype = "FLT4S",
  absVolumeAreaSaveName = "VolumeArea_AbsoluteScale.csv",
  savedir = tempdir(),
  saveAreaCT = FALSE,
  alerts = TRUE
)

Arguments

Details

We strongly recommend that prior to choosing any of the model options, you familiarize yourself with the underlying concepts of dBBMMs and dBGBs by reading the corresponding publications from Kranstauber et al. (2012) for dBBMMs and Kranstauber et al. (2014) for dBGBs. Please see the full reference details in the 'References' section below.

Here we provide a very short overview of the most important points from the listed references. Dynamic BBMMs and dBGBs are an improvement over traditional UD calculation methods, as they are able to integrate a temporal component to the movement data and explicitly model the movement trajectory between two consecutive locations. The dBBMM does this by defining the variance of the Brownian motion between two locations and estimating how irregular the movement path is. Importantly, in dBBMMs the movement between locations is assumed to be diffusive and isotropic. Effectively, this means that the movement between two locations is equally likely to happen in all directions. Dynamic BGBs allow the incorporation of a directional bias in the movement path analysis of bridging models. Kranstauber et al.'s Bivariate Gaussian Bridges model does this by factorizing the diffusion, i.e. the Brownian variance estimates, into the direction towards and perpendicular to a straight line to the next location, resulting in two normally distributed probability densities for each direction.

In either model choice, long time gaps between two consecutive detections can therefore present an issue, as these long time gaps create a very high uncertainty in regard to your animal's whereabouts between two locations. However, given the different underlying assumptions in regard to the diffusion process in each model, longer time gaps can cause different problematic outputs. We would like to thank Dr. Anne K. Scharf for the further explanations regarding the model choice. In short, dBBMM does not distinguish between the animal changing movement in direction or speed between two locations. But, dBGBs make a difference between the animal changing movement in direction and in speed between two locations. Long time gaps in dBGBs can therefore create stronger uncertainty in change in direction so that the outputs of your model runs might show biologically unrealistic, very narrow and long peaks along or perpendicular to the trajectory between two locations, whereas such a case in dBBMMs would create a uninformative, inflated circular area with a large diameter within which your animal might have been (i.e. pretty much anywhere, which is not informative).

The solution to this problem is in both cases to remove the variance of the segments corresponding to the large time gaps. This can be done using the 'move' package, and is directly implemented in this function already (please see params timeDiffLong and timeDiffUnits above). However, the ideal settings may be different based on your model choice due to the reasons explained above. And, based on your data structure, your data may be inadequate for your model choice. It is therefore imperative that you understand the assumptions of the different models and how they relate to your own data.

Once you have decided, which model is most appropriate for your data, we here provide the movegroup::movegroup() function that automates the following steps in calculating dBBMM/dBGB-based UDs:

Step 1. Filters individuals. Remove those individuals for which there are insufficient data i.e. number of re-locations is smaller than the window size parameter value (defaults: 23 for model "dbbmm" and 31 for model "dBGB").

Step 2. Generates universal raster. Based on all remaining data, a universal raster is generated where the calculated UDs are plotted into.

Step 3. Loops through individuals. Individuals are looped through to construct individual-level movement models (on an absolute scale).

See www.GitHub.com/SimonDedman/movegroup for issues, feedback, and development suggestions.

install_git('https://gitlab.com/bartk/move.git') #Installs 'move' development version

The movegroup package provides additional functions to automate and simplify the visualisation and quantification of space use data of a group or populations of animals. When used together, the order of these functions would be: movegroup, (stitchraster if required), scaleraster, (alignraster if required then stitchraster again) , plotraster.

Errors and their origins:

1. Error in .local(object, raster, location.error = location.error, ext = ext: Higher y or x grid not large enough, consider extending the raster in that direction or enlarging the ext argument. Increase buffpct, e.g. to 3.

2. Error in .data[[dttm]]: Must subset the data pronoun with a string, not a <POSIXct/POSIXt> object. Use "ColName" not dataframe$ColName syntax for Datetime, ID, Lat, Lon.

3. Error in splice(dot_call(capture_dots, frame_env = frame_env, named = named,: object 'DateTime' not found. Use "ColName" not ColName syntax for Datetime, ID, Lat, Lon.

4. Error in .local(object, raster, location.error = location.error, ext = ext: Higher x grid not large enough, consider extending the raster in that direction or enlarging the ext argument. Try "buffpct = 1," , then larger e.g. 3, if still getting the error.

5. cannot allocate vector of size (BIG) Gb: Increase rasterResolution value.

6. In min/max: No non-missing arguments to min; returning Inf: likely not enough memory, increase rasterResolution value.

7. Error in tmp[[i]]: subscript out of bounds. dbbmmwindowsize may be too large relative to nrow of that individual. Try lowering movemargin (default 11, has to be odd) and then lowering dbbmmwindowsize (default 23, has to be >=2*movemargin, has to be odd).

8. Error in validityMethod(as(object, superClass)): The used raster is not a UD (sum unequal to 1), sum is NaN. Potentially from memory overrun from large datasets. Close all other programs, restart your session, remove other objects from R, and try again, watching the RAM usage piechart by the Environment tab. If unsuccessful, run function in chunks of individuals until all (of length >= windowsize) are saved to disc as asc files, then produce final summary stats with stitchraster.

9. Error in validityMethod(as(object, superClass)) : The used raster is not a UD (sum unequal to 1), sum is: NaN One possible cause is loss of accuracy due to writing raster to disk with datatype FLT4S this can be solved preventing disk usage or changing data type. Same error message as in nr. 8 above. This error can also occur if you are calculating dBGBs and set your timeDiffLong shorter than the shortest segment. Check your time gaps between locations and compare to timeDiffLong, check the timeDiffUnits parameter and make sure you chose the correct unit. Try shorter timeDiffLong options.

10. Function runs but no findable output: The default directory into which outputs are saved (i.e. savedir) is set to 'tempdir()'. This means, that if you forget to change this parameter, all outputs will be saved to your system's temporary folder.

Value

Individual-level utilization distributions, saved as rasters, as well as calculated volume area estimates for 50 and 95pct contours, saved in a .csv file. Motion variance csvs per individual ("MotionVariance.csv"), see move::brownian.motion.variance.dyn or move::dynBGBvariance. No processed object is returned, i.e. bad: "objectname <- movegroup()", good: "movegroup()".

Author(s)

Simon Dedman, simondedman@gmail.com

Maurits van Zinnicq Bergmann, mauritsvzb@gmail.com

Vital Heim, vital.heim@gmail.com

References

Kranstauber, B., Kays, R., LaPoint, S. D., Wikelski, M. and Safi, K. (2012), A dynamic Brownian bridge movement model to estimate utilization distributions for heterogeneous animal movement. Journal of Animal Ecology. doi: 10.1111/j.1365-2656.2012.01955.x

Kranstauber, B., Safi, K., and Bartumeus, F. (2014). Bivariate Gaussian bridges: directional factorization of diffusion in Brownian bridge models. Movement Ecology. https://doi.org/10.1186/2051-3933-2-5

Kranstauber, B., M. Smolla & A. K. Scharf. 2019. Move: visualising and analysing animal track data. R package version 4.2.4 (at 2023-08-15). https://CRAN.R-project.org/package=move.

Examples

# load data
data("TracksCleaned")
# run function
movegroup(
  data = TracksCleaned,
  ID = "Shark",
  Datetime = "Datetime",
  Lat = "Lat",
  Lon = "Lon",
  savedir = tempdir()
)

Plots a group-level utilization distribution

Description

Plots 50 and 95% utilization distribution (UD) contours of a group-level UD raster on a spatial map background. Contains functionality to also visualize geographic locations of individual listening stations (e.g., acoustic receivers) as well as the entire surface UD.

Usage

plotraster(
  x = file.path("Scaled", "All_Rasters_Scaled_Weighted_UDScaled.asc"),
  crsloc = NULL,
  xlatlon = NULL,
  locationpoints = NULL,
  calcCOA = FALSE,
  pointsincontourssave = NULL,
  trim = TRUE,
  myLocation = NULL,
  googlemap = FALSE,
  gmapsAPI = NULL,
  stadiaAPI = NULL,
  expandfactor = 1.6,
  mapzoom = NULL,
  mapsource = "google",
  maptype = "satellite",
  generalUDpct = 0.95,
  coreUDpct = 0.5,
  contour1colour = "red",
  contour2colour = "orange",
  positionscolour = "white",
  positionsalpha = 0.33,
  positionssize = 0.1,
  COAcolour = "black",
  COAalpha = 1,
  COAshape = 4,
  COAsize = 1,
  plottitle = NULL,
  plotsubtitle = "Scaled contours",
  legendtitle = "Percent UD Contours",
  plotcaption = paste0("movegroup, ", lubridate::today()),
  axisxlabel = "Longitude",
  axisylabel = "Latitude",
  legendposition = c(0.105, 0.8),
  fontsize = 12,
  fontfamily = "Times New Roman",
  filesavename = paste0(lubridate::today(), "_UD-contours.png"),
  savedir = tempdir(),
  receiverlats = NULL,
  receiverlons = NULL,
  receivernames = NULL,
  receiverrange = NULL,
  recpointscol = "black",
  recpointsfill = "white",
  recpointsalpha = 0.5,
  recpointssize = 1,
  recpointsshape = 21,
  recbufcol = "grey75",
  recbuffill = "grey",
  recbufalpha = 0.5,
  reclabcol = "black",
  reclabfill = NA,
  reclabnudgex = 0,
  reclabnudgey = -200,
  reclabpad = 0,
  reclabrad = 0.15,
  reclabbord = 0,
  surface = TRUE,
  surfaceUDpct = 0.95,
  cropsavedimage = FALSE,
  savewidth = 6,
  saveheight = NULL
)

Arguments

Details

For plottitle, you can use the term 'home range' when an animal can be detected wherever it goes i.e. using GPS, satellite or acoustic telemetry whereby it is known that acoustic receivers cover the entire home range of the study species. This term is problematic when applied to a passive acoustic telemetry setting where an array of non-overlapping receivers are used to assess local space use patterns i.e. the home range is bigger than the coverage by the acoustic array.

Please be aware,

Errors and their origins:

1. Error in gzfile(file, "rb") : cannot open the connection. In addition: Warning message: In gzfile(file, "rb"): cannot open compressed file '/var/folders/dl/etc/ggmap/index.rds', probable reason 'No such file or directory'. Cause: index.rds may not have been created, due to a problem with ggmap::get_map, likely due to your API key failing silently. Filename too long - solve with filename = "whatever" but doesn't do anything. Added issue in github: https://github.com/dkahle/ggmap/issues/346 . API key help: https://github.com/dkahle/ggmap/issues/235 .

2. trying to read file: All_Rasters_Scaled_Weighted_UDScaled.asc: Error in CPL_read_gdal( as.character(x), as.character(options), as.character(driver),: file not found. Check x is correct.

3. Error in grid.Call.graphics...: Empty raster: mapzoom likely set too low, returning no tiles. Increase mapzoom number.

4. Not Found (HTTP 404). Failed to acquire tile /...png: Tiles are unavailable for ocean, and may be unavailable at the chosen zoom level for the specific region of interest. Inspect the output map and try a lower level (number) of mapzoom.

How to get Google map basemaps

(from https://www.youtube.com/watch?v=O5cUoVpVUjU):

1. Sign up with dev console: a. You must enter credit card details, but won’t be charged if your daily API requests stay under the limit. b. Follow the link: https://console.cloud.google.com/projectselector2/apis/dashboard?supportedpurview=project c. Sign up for Google cloud account (it may auto populate your current gmail), click agree and continue. d. Click the navigation email in the top left corner and click on Billing. e. Create a billing account – they will NOT auto charge after trial ends. f. Enter information, click on 'start my free trial'. They may offer a free credit for trying out their service. More pricing details: https://mapsplatform.google.com/pricing/ . g. Click “Select a Project” then “New project” in the top right corner. h. Enter Project Name, leave Location as is, click “Create”. i. You should now see your project name at the top, where the drop-down menu is.

2. Enable Maps and Places API: a. Click 'Library' on the left. b. In the search field type “Maps” . c. Scroll down, click “Maps Java Script API”. d. Click Enable. e. Click 'Library' again, search “Places”, click on “Places API”. f. Click Enable.

3. Create Credentials for API Key: a. Return to 'APIs & Services' page. b. Click on Credentials. c. At the top click 'Create Credentials > API Key'. d. API key should pop up with option to copy it. e. You can restrict the key if you want by following steps 4 & 5 here: https://www.youtube.com/watch?v=O5cUoVpVUjU&t=232s

How to get Stadia Maps basemaps

https://client.stadiamaps.com/signup/ Get an account, copy your API key.

Value

Individual-level utilization distributions, saved as rasters, as well as calculated volume area estimates for 50 and 95pct contours, saved in a .csv file.

Author(s)

Simon Dedman, simondedman@gmail.com

Maurits van Zinnicq Bergmann, mauritsvzb@gmail.com

Vital Heim, vital.heim@gmail.com

Examples

# Having run the movegroup and scaleraster function examples:
plotraster(
  x = file.path(tempdir(), "Scaled", "All_Rasters_Scaled_Weighted_UDScaled.asc"),
  mapzoom = 14,
  mapsource = "stamen",
  maptype = "terrain",
  savedir = file.path(tempdir(), "Plot"),
  xlatlon = file.path(tempdir(), "Scaled", "All_Rasters_Scaled_Weighted_LatLon.asc"),
  locationpoints = TracksCleaned |> dplyr::rename(lat = "Lat", lon = "Lon"),
  pointsincontourssave = file.path(tempdir(), "Scaled", "pointsincontours.csv"))

Scales individual utilization distribution (UD) rasters and volume area estimates

Description

Scales individual-level utilization distribution (UD) rasters from 0 to 1 to facilitate interpretation as relative intensity of utilization (as opposed to absolute), making comparisons across individuals and interpretations at the group level more straightforward. Subsequently, scaled individual-level rasters are aggregated to create a single group-level UD raster. See www.GitHub.com/SimonDedman/movegroup for issues, feedback, and development suggestions. There is an option to account for bias in acoustic receiver array spatial representation (see Details).

Usage

scaleraster(
  path = NULL,
  pathsubsets = NULL,
  pattern = ".asc",
  weighting = 1,
  format = "ascii",
  datatype = "FLT4S",
  bylayer = TRUE,
  overwrite = TRUE,
  scalefolder = "Scaled",
  scaledweightedname = "All_Rasters_Scaled_Weighted",
  crsloc = NULL,
  returnObj = FALSE
)

Arguments

Details

We here provide the movegroup::scaleraster() function that automates the following steps to scale individual-level utilisation distribution (UD) rasters from 0 to 1 for interpretation as relative intensity of utilization, and to subsequently aggregate scaled individual-level rasters into one single group-level UD raster:

Step 1. Scale rasters. Individual-level UD rasters are scaled from 0 to 1 by dividing each raster by the maximum probability density value occurring within the raster set.

Step 2. Aggregate into a group-level raster. Scaled individual-level rasters are summed to create a single group-level UD raster.

Step 3. Re-scale to 0 to 1. The group-level raster is divided by its own maximum value.

Step 4. Weight raster (optional). The scaled group-level UD raster is divided by the specified weighting factor(s). Note that this is only useful if you want to account for an unbalanced listening station (e.g., acoustic receivers) array and have split up the study site and receivers in regions, and have run the movegroup() for each regional data set separately. See van Zinnicq Bergmann et al. 2022 (https://doi.org/10.1016/j.biocon.2022.109469) for example. If not applicable, choose a value of "1".

Step 5. Standardize raster. Standardize the potentially weighted and scaled group-level UD raster so that its values sum to 1.

Step 6. Export as latlon CRS. Change crs to latlon for plotting and calculation purposes, save file, continue.

Step 7. Estimate 50 and 95pct contour volume areas. For each scaled individual-level UD raster, estimate 50 and 95pct contour volume areas, as well as their mean and standard deviation. Additionally, the 50 and 95pct volume area is estimated for the group-level UD raster.

Step 8. Export the projected-CRS group-level raster.

Errors and their origins:

1. Error in (function (cond): error in evaluating the argument 'x' in selecting a method for function 'res': subscript out of bounds. Probably path can't find any files of type=pattern: check you used a terminal slash in savedir in movegroup, and that path has files of type=pattern.

2. Error in if (substr(x = pathsubsets, start = nchar(pathsubsets), stop = nchar(pathsubsets))==: argument is of length zero: pathsubsets is wrong. Try setting to same as path. NULL does this.

3. Error in gzfile(file, "rb"): cannot open compressed file 'CRS.Rds', probable reason 'No such file or directory': crsloc is wrong. Try setting to same as path. NULL does this.

4. In min/max: No non-missing arguments to min; returning Inf: likely not enough memory, increase rasterResolution value.

Value

Scaled and weighted individual-level and group-level utilization distributions saved as rasters. Scaled 50 and 95pct contour volume area estimates (in km2) for individuals and the group , saved in .csv format. Latlon raster.

Author(s)

Simon Dedman, simondedman@gmail.com

Maurits van Zinnicq Bergmann, mauritsvzb@gmail.com

Vital Heim, vital.heim@gmail.com

Examples

# Having run the movegroup function example:
scaleraster(path = tempdir())

# Weighted by number of positions per ID, fewer locations = lower Weighting value = higher final 
# UD values after dividing by Weighting. This scales all IDs up to match the group max.
Weighting <- TracksCleaned |>
 dplyr::group_by(Shark) |>
 dplyr::summarise(N = n()) |> 
 dplyr::filter(N > 23) |> 
 dplyr::mutate(N = N / max(N, na.rm = TRUE)) |> 
 dplyr::pull(N)
 
 scaleraster(path = tempdir(), weighting = Weighting)

Stitch together movegroup data individuals core and general use areas

Description

If over-large datasets cause RAM crashes for movegroup, one can run batches of individuals in movegroup then join the individual saved area.ct csv files.

Usage

stitchraster(
  data = NULL,
  ID = NULL,
  absVolumeAreaSaveName = "VolumeArea_AbsoluteScale.csv",
  savedir = tempdir()
)

Arguments

Details

See www.GitHub.com/SimonDedman/movegroup for issues, feedback, and development suggestions. Install 'move' development version with: remotes::install_git('https://gitlab.com/bartk/move.git')

Parameters values should match those used in movegroup.

Value

Calculated volume area estimates for 50 and 95pct contours csv.

Author(s)

Simon Dedman, simondedman@gmail.com